Tootfinder

The Hammock Plot: Where Categorical and Numerical Data Relax Together
Matthias Schonlau, Tiancheng Yang
https://arxiv.org/abs/2506.13630 https://

The Hammock Plot: Where Categorical and Numerical Data Relax Together
Effective methods for visualizing data involving multiple variables, including categorical ones, are limited. The hammock plot (Schonlau, 2003) visualizes both categorical and numerical variables using parallel coordinates. We introduce the Stata implementation hammock. We give numerous examples that explore highlighting, missing values, putting axes on the same scale, and tracing an observation across variables. Further, we introduce parallel univariate plots as an edge case of hammock plots. …

Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
Zhongyuan Lyu, Yuqi Gu
https://arxiv.org/abs/2506.07167 https://…

Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
Latent class models are widely used for identifying unobserved subgroups from multivariate categorical data in social sciences, with binary data as a particularly popular example. However, accurately recovering individual latent class memberships and determining the number of classes remains challenging, especially when handling large-scale datasets with many items. This paper proposes a novel two-stage algorithm for latent class models with high-dimensional binary responses. Our method first i…

This https://arxiv.org/abs/2310.01147 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimizing Symbol Visibility through Displacement
In information visualization, the position of symbols often encodes associated data values. When visualizing data elements with both a numerical and a categorical dimension, positioning in the categorical axis admits some flexibility. This flexibility can be exploited to reduce symbol overlap, and thereby increase legibility. In this paper, we initialize the algorithmic study of optimizing symbol legibility via a limited displacement of the symbols. Specifically, we consider closed unit square …

Random Utility with Aggregated Alternatives
Yuexin Liao, Kota Saito, Alec Sandroni
https://arxiv.org/abs/2506.00372 https://arxiv.org…

Random Utility with Aggregated Alternatives
This paper studies when discrete choice data involving aggregated alternatives such as categorical data or an outside option can be rationalized by a random utility model (RUM). Aggregation introduces ambiguity in composition: the underlying alternatives may differ across individuals and remain unobserved by the analyst. We characterize the observable implications of RUMs under such ambiguity and show that they are surprisingly weak, implying only monotonicity with respect to adding aggregated …